Keynote lecture ESCAPE 2016

7/25/2019 Keynote lecture ESCAPE 2016

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P-Graph Approach to Carbon-Constrained

Energy Planning Problems

Raymond R. Tan1, Kathleen B. Aviso1, Dominic C.Y. Foo2

1Chemical Engineering Department

De La Salle University, Manila, Philippines2Chemical Engineering Department

University of Nottingham Malaysia, Selangor, Malaysia

26th European Symposium on Computer-Aided Process

Engineering, June 12 - 15, 2016 Portoro, Slovenia


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My co-authors


Engineering, June 12 - 15, 2016 Portoroz, Slovenia

2

Prof. Dominic C.Y. Foo

University of Nottingham Malaysia Campus

Prof. Raymond Tan

De La Salle University, Manila, Philippines


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Outline of Presentation

Global Energy/Climate Scenario

Generalized Source/Sink Model

P-graph methodology

Problem structure in planning CO2 abatement technologies

Case study

Results, Conclusions and Future work



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Exceeding the Limits(Rockstrom et al., 2009)

Atmospheric CO2 levels

now exceed 400 ppm

Global GHG emissions

continue to grow, fuelled

by economic and

demographic trends

Climate change has

complex links with other

issues e.g.,

biodiversity loss, water

stress, land use



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Selected Energy Statistics

Indicator Value Source

Projected primary energy demand

2040

750 - 860 x 1018 J IEA (2015)

Projected CO2 emissions in 2040 45 x 109 t/y EIA (2016)

World petroleum reserves-to-

production (R/P) ratio in 2001

39 y BP (2002)

Projected global consumption of oil,

NG and coal in 2020

255, 178 and 129 x

1018 J

EIA (2002)

Estimated land requirement to

supply bioenergy

0.74-1.94 ha/capita Nonhebel (2005)



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Some Recent Energy Trends

Strong demand growth for reliable, low-cost energy, especially inthe developing world, will continue for decades

Nuclear energy has fallen into disfavor as a result of the

Fukushima crisis in 2011

Non-conventional gas is now accessible; CO2 emissions, ratherthan supply, will be the constraint to its use.

Transport trends (EVs and HEVs) may cause shifts in patterns of

energy use

There still remain inherent limitations to various forms ofrenewable energy



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CO2 Abatement Technology Wedges(Source: International Energy Agency)



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Basic Problem Pattern

Minimize use of scarce, high-quality stream

Each stream source has fixed quality and quantity characteristics

Each stream demand has fixed quality and quantity requirements

Quality index is inverse and follows a linear mixing rule




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Source: A streamwhich containsthe targeted

species. Eachsource has:Flowrate FiQuality QiQuality load:

mi = Fi Qi

Source/sink representation

Sink: An existingprocess unit/equipment that canaccept a source.

Each sink has:Flowrate FjQuality Qj where:Qj

min Qj Qjmax

Load capacity:mi = Fi Qi

Source i

j = 1

j = 2

Sinkj

j = 3

i = 1

i = 2

i= 3

(El-Halwagi, 2006)



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Streams and Qualities in Pinch Analysis

Flows Qualities Examples/Problems

Heat Temperature

Heat integration (1971, 1979)

Total site integration (1984)

Integration of thermal equipments (1982)

Mass Concentration

Mass integration (1989)

Water/Hydrogen management(1994,1996)

Pollution prevention/Treatment networks

Mass Properties Recycle/reuse networks (2004)

Steam Pressure Cogeneration (1993, 2008)

Energy CO2 Carbon-constrained energy planning (2007)

Mass Time Supply chain management (2002)

Energy TimeStand-alone energy system (2007)

Isolated power system (2007)



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The Solution Strategy

In the simplest case, the objective is to

identify the minimum external resource

needed by maximising use of internal

sources.

Problem components:

Targeting identification of optimal

resource budget

Network design matching sources

and sinks to achieve target

i = 1

i = 2

i = 3

i =NSR

SOURCE

j = 1

j = 2

j = 3

j =NSK

SINK




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Optimization Model

Objective Function

Balance at Sink

Balance at Source

Quality Constraint



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P-graph Methodology

Process graph or p-graph is a graph theoretic method

developed for process network synthesis (Friedler et al., 1992,

1993)

P-graph utilizes 3 algorithms to identify the optimal network

structure

MSG maximal structure generation SSG solution structure generation

ABB advanced branch and bound

P-graph is a graphical representation of

matrix calculations such as MILP Provides near optimal solutions

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RM1

P1

RM2OPERATING UNIT




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Recent applications of P-graph

Application Authors

Vehicle maintenance scheduling Adonyi et al. (2013)

Industrial symbiosis networks Aviso et al., (2015a)

Allocation of in economic systems during crises Aviso et al., (2015b)

Planning of CO2 capture and storage systems Chong et al. (2014)

Planning of building evacuation during

emergenciesGarcia-Ojeda et al. (2012)

Planning of supply chains in the Energy-Water-

Food Nexus Heckl et al. (2015b)

Open-structure biomass networks Lam et al. (2013)

Renewable energy systems for cities Maier and Narodoslawsky (2014)

Biomass supply chains with consideration of

occupational safety Ng et al. (2015)

Operation of polygeneration plants underabnormal conditions Tan et al. (2014)

Inoperability risk allocation in urban infrastrcuture Tan et al. (2015)

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Problem Statement

There are Mnumber of energy sources with supply limit and

specified CO2 intensity

There are Nenergy demands with given energy requirement and

maximum tolerable CO2 intensity

A high quality energy source (e.g. zero CO2 intensity) is externally

available

Determine the optimal allocation of energy sources which minimizes

the zero-carbon energy source and meets energy demand of sinks



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P-Graph Source-Sink Model

SOURCE 1

S1

SOURCE 2

S2

DEMAND 1

D1

DEMAND 2

D2

Superstructure for 2 sources and

2 sinks

P-graph representation for 2

sources and 2 sinks



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AvailableSource



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Energy

Demands 19


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QualityConstraints



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Flow ofresources



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Case Study(Tan and Foo, 2007)

Energy Source Emission Factor

(t CO2/TJ)

Available Sources

(TJ)

Coal 105 600,00

Oil 75 800,00

Natural Gas 55 200,00

Zero-carbon 0 > 400,00

Total > 2,000,000

Energy Demand Emission Limit

(x 106 t CO2)

Expected Consumption

(TJ)

Region 1 20 1,000,000

Region 2 20 400,000

Region 3 60 600,000

Total 100 2,000,000



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P-graph Representation

ZERO

CARBON

NATURAL

GASOILCOAL

REGION 3REGION 2REGION 1

ENERGY

SOURCE

ENERGY

DEMAND26th European Symposium on Computer-Aided Process


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Optimal Solution

Source Region 1

(TJ)

Region 2

(TJ)

Region 3

(TJ)

Total

(TJ)

Coal 0.00 0.00 186,667 186,667

Oil 266,667 120,000 413,333 800,000

Natural Gas 0.00 200,000 0.00 200,000

Zero Carbon 733,333 80,000 0.00 813,333

Total 1,000,000 400,000 600,000

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Near Optimal Network

Source Region 1 Region 2 Region 3 Total

Coal 0.00 0.00 540,000 540,000

Oil 266,667 164,000 0.00 430,667

Natural Gas 0.00 140,000 60,000 200,000

Zero Carbon 733,333 96,000 0.00 829,333

Total 1,000,000 400,000 600,000

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Near Optimal Network

Source Region 1 Region 2 Region 3 Total

Coal 0.00 0.00 540,000 540,000

Oil 266,667 164,000 0.00 430,667

Natural Gas 0.00 140,000 60,000 200,000

Zero Carbon 733,333 96,000 0.00 829,333

Total 1,000,000 400,000 600,000

2%

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Conclusions and Future Work

The P-graph model is equivalent to alternative techniques (e.g.

CEPA and linear programming)

P-graph provides n-best solutions

Alternative structures may be important in real life decision-making

Future work will look into:

integration of multiple quality indicators

evaluation of robustness of alternative solutions

extensions to other problem variants



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References

Friedler, F., Tarjan, K., Huang, Y. W., and Fan, L. T.,1992a, Graph-theoretic approach to process

synthesis: axioms and theorems. Chemical Engineering Science, 47: 1973-1988.

Friedler, F., Tarjan, K., Huang, Y. W. and Fan, L. T., 1992b, Combinatorial algorithms for process

synthesis. Computers & Chemical Engineering, 16: 313 320.

Friedler, F., Tarjan, K., Huang, Y. W., & Fan, L. T., 1993, Graph-theoretic approach to process

synthesis: polynomial algorithm for maximal structure generation. Computers & Chemical

Engineering, 1993; 17: 929-942.

Foo, D. C., Tan, R. R., and Ng, D. K. ,2008, Carbon and footprint-constrained energy planning using

cascade analysis technique. Energy, 33(10), 1480-1488.Foo, D. C., Tan, R. R. (2015). A review on process integration techniques for carbon emissions and

environmental footprint problems. Process Safety and Environmental Protection.

Lam, H. L., 2013, Extended P-graph applications in supply chain and process network synthesis.

Current Opinion in Chemical Engineering, 2(4), 475-486.

Tan, R. R., and Foo, D. C., 2007, Pinch analysis approach to carbon-constrained energy sector

planning. Energy, 32(8), 1422-1429.



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Thanks for your attention

Comments and questions are welcome

Or contact me via e-mail:

Kathleen B. Aviso

[email protected]

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Keynote lecture ESCAPE 2016

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